<?xml version='1.0' encoding='utf-8'?>
<eprints xmlns='http://eprints.org/ep2/data/2.0'>
  <eprint id='https://researchdata.gla.ac.uk/id/eprint/1393'>
    <eprintid>1393</eprintid>
    <rev_number>22</rev_number>
    <documents>
      <document id='https://researchdata.gla.ac.uk/id/document/5624'>
        <docid>5624</docid>
        <rev_number>5</rev_number>
        <files>
          <file id='https://researchdata.gla.ac.uk/id/file/29828'>
            <fileid>29828</fileid>
            <datasetid>document</datasetid>
            <objectid>5624</objectid>
            <filename>data.zip</filename>
            <mime_type>application/zip</mime_type>
            <hash>28a5c9cef71c20174eb47640e5c13a95</hash>
            <hash_type>MD5</hash_type>
            <filesize>2198572590</filesize>
            <mtime>2023-01-25 11:06:39</mtime>
            <url>https://researchdata.gla.ac.uk/1393/1/data.zip</url>
          </file>
        </files>
        <eprintid>1393</eprintid>
        <pos>1</pos>
        <placement>1</placement>
        <mime_type>application/zip</mime_type>
        <format>Mixed</format>
        <language>en</language>
        <security>public</security>
        <license>cc_by_4</license>
        <main>data.zip</main>
        <content>data</content>
      </document>
      <document id='https://researchdata.gla.ac.uk/id/document/5625'>
        <docid>5625</docid>
        <rev_number>5</rev_number>
        <files>
          <file id='https://researchdata.gla.ac.uk/id/file/29830'>
            <fileid>29830</fileid>
            <datasetid>document</datasetid>
            <objectid>5625</objectid>
            <filename>ReadMe.txt</filename>
            <mime_type>text/plain</mime_type>
            <hash>c8c3172a3c9ddc70fa4a4ce8a6e8ef4e</hash>
            <hash_type>MD5</hash_type>
            <filesize>911</filesize>
            <mtime>2023-01-25 11:10:25</mtime>
            <url>https://researchdata.gla.ac.uk/1393/2/ReadMe.txt</url>
          </file>
        </files>
        <eprintid>1393</eprintid>
        <pos>2</pos>
        <placement>2</placement>
        <mime_type>text/plain</mime_type>
        <format>Text</format>
        <language>en</language>
        <security>public</security>
        <license>cc_by_4</license>
        <main>ReadMe.txt</main>
        <content>readme</content>
      </document>
      <document id='https://researchdata.gla.ac.uk/id/document/5637'>
        <docid>5637</docid>
        <rev_number>1</rev_number>
        <files>
          <file id='https://researchdata.gla.ac.uk/id/file/29872'>
            <fileid>29872</fileid>
            <datasetid>document</datasetid>
            <objectid>5637</objectid>
            <filename>indexcodes.txt</filename>
            <mime_type>text/plain</mime_type>
            <hash>ad07d2a03637126673427e3190a4f933</hash>
            <hash_type>MD5</hash_type>
            <filesize>433</filesize>
            <mtime>2023-01-25 16:26:30</mtime>
            <url>https://researchdata.gla.ac.uk/1393/3/indexcodes.txt</url>
          </file>
        </files>
        <eprintid>1393</eprintid>
        <pos>3</pos>
        <placement>3</placement>
        <mime_type>text/plain</mime_type>
        <format>other</format>
        <formatdesc>Generate index codes conversion from Text to indexcodes</formatdesc>
        <language>en</language>
        <security>public</security>
        <main>indexcodes.txt</main>
        <relation>
          <item>
            <type>http://eprints.org/relation/isVersionOf</type>
            <uri>https://researchdata.gla.ac.uk/id/document/5625</uri>
          </item>
          <item>
            <type>http://eprints.org/relation/isVolatileVersionOf</type>
            <uri>https://researchdata.gla.ac.uk/id/document/5625</uri>
          </item>
          <item>
            <type>http://eprints.org/relation/isIndexCodesVersionOf</type>
            <uri>https://researchdata.gla.ac.uk/id/document/5625</uri>
          </item>
        </relation>
      </document>
    </documents>
    <eprint_status>archive</eprint_status>
    <userid>5</userid>
    <dir>disk0/00/00/13/93</dir>
    <datestamp>2023-01-27 16:18:22</datestamp>
    <lastmod>2023-03-06 09:56:29</lastmod>
    <status_changed>2023-02-06 10:05:41</status_changed>
    <type>data_collection</type>
    <metadata_visibility>show</metadata_visibility>
    <creators>
      <item>
        <name>
          <family>Martinelli</family>
          <given>Cristiano</given>
        </name>
        <enlightenid>67100</enlightenid>
      </item>
      <item>
        <name>
          <family>Coraddu</family>
          <given>Andrea</given>
        </name>
      </item>
      <item>
        <name>
          <family>Cammarano</family>
          <given>Andrea</given>
        </name>
        <enlightenid>33341</enlightenid>
        <orcid>0000-0002-8222-8150</orcid>
      </item>
    </creators>
    <uniqueid>glaresearchdata:2023-01-25-1393</uniqueid>
    <title>Approximating piecewise nonlinearities in dynamic systems with sigmoid functions: Advantages and limitations</title>
    <divisions>
      <item>30300000</item>
    </divisions>
    <abstract>The dataset contains numerical data regarding the analysis of a nonlinear two-degree-of-freedom mechanical system with a piecewise smooth-continuous stiffness characteristic, as presented in the paper: Martinelli.C., Coraddu A., and Cammarano A., Approximating piecewise nonlinearities in dynamic systems with sigmoid functions: Advantages and limitations, Nonlinear Dynamics, 2023. In particular, the dataset contains the results of the numerical continuation/integration procedures which were used to create the bifurcation diagram, the frequency response diagrams, the attractors, and the basins of attraction of the systems, as shown in the above-mentioned paper.</abstract>
    <date>2023-01-25</date>
    <publisher>University of Glasgow</publisher>
    <id_number>10.5525/gla.researchdata.1393</id_number>
    <data_type>
      <item>Code</item>
      <item>Image</item>
      <item>Text</item>
    </data_type>
    <copyright_holders>
      <item>University of Glasgow</item>
    </copyright_holders>
    <funding>
      <item>
        <project_code>311655</project_code>
        <project_name>Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19</project_name>
        <investigator_name>Andrea Cammarano</investigator_name>
        <funder_name>Engineering and Physical Sciences Research Council (EPSRC)</funder_name>
        <funder_code>EP/V036777/1</funder_code>
        <investigator_dept>ENG - Autonomous Systems &amp; Connectivity</investigator_dept>
      </item>
      <item>
        <project_code>313196</project_code>
        <project_name>NERC Strategic Programme Call</project_name>
        <investigator_name>Jaime Toney</investigator_name>
        <funder_name>Natural Environment Research Council (NERC)</funder_name>
        <funder_code>NE/W005042/1</funder_code>
        <investigator_dept>GES - Geography</investigator_dept>
      </item>
    </funding>
    <pending>FALSE</pending>
    <language>EN</language>
    <retention_date>2033-01-25</retention_date>
    <retention_action>R</retention_action>
    <dataset_origin>
      <item>file_transfer</item>
    </dataset_origin>
    <ingest_data>
      <item>
      </item>
      <item>
      </item>
    </ingest_data>
    <archive_data>
      <item>
      </item>
      <item>
      </item>
    </archive_data>
    <ethics_consent_required>FALSE</ethics_consent_required>
    <request_copy>FALSE</request_copy>
    <repo_link>
      <item>
        <title>Approximating piecewise nonlinearities in dynamic systems with sigmoid functions: advantages and limitations</title>
        <link>https://eprints.gla.ac.uk/id/eprint/293494</link>
      </item>
    </repo_link>
  </eprint>
</eprints>
